Stand-Alone AI Technology Reduces Radiologists’ Screening Mammography Workloads by 90%

Researchers from the Hospital Universitario Reina Sofía (Cordova, Spain) conducted a study to retrospectively evaluate the stand-alone performance of an AI system as an independent reader of DM and DBT screening examinations. Consecutive screening-paired and independently read DM and DBT images were collected and an AI system computed a cancer risk score (range, 1–100) for the DM and DBT examinations independently. AI stand-alone performance was measured using the area under the receiver operating characteristic curve (AUC) and sensitivity and recall rate at different operating points selected to have non-inferior sensitivity compared with the human readings (non-inferiority margin, 5%). The recall rate of AI and the human readings were compared using a McNemar test.

A total of 15 999 DM and DBT examinations (113 breast cancers, including 98 screen-detected and 15 interval cancers) from 15 998 women were evaluated. AI achieved an AUC of 0.93 (95% CI: 0.89, 0.96) for DM and 0.94 (95% CI: 0.91, 0.97) for DBT. For DM, AI achieved non-inferior sensitivity as a single (58.4%; 66 of 113; 95% CI: 49.2, 67.1) or double (67.3%; 76 of 113; 95% CI: 58.2, 75.2) reader, with a reduction in recall rate (P < .001) of up to 2% (95% CI: −2.4, −1.6). For DBT, AI achieved non-inferior sensitivity as a single (77%; 87 of 113; 95% CI: 68.4, 83.8) or double (81.4%; 92 of 113; 95% CI: 73.3, 87.5) reader, but with a higher recall rate (P < .001) of up to 12.3% (95% CI: 11.7, 12.9).

The researchers concluded that AI could replace radiologists’ readings in breast screening, achieving a non-inferior sensitivity, with a lower recall rate for DM but a higher recall rate for DBM.

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Hospital Universitario Reina Sofía